Contribution of a Non-β-Cell Source to β-Cell Mass during Pregnancy
Bibliographic record
Abstract
β-cell mass in the pancreas increases significantly during pregnancy as an adaptation to maternal insulin resistance. Lineage tracing studies in rodents have presented conflicting evidence on the role of cell duplication in the formation of new β-cells during gestation, while recent human data suggest that new islets are a major contributor to increased β-cell mass in pregnancy. Here, we aim to: 1) determine whether a non-β-cell source contributes to the appearance of new β-cells during pregnancy and 2) investigate whether recapitulation of the embryonic developmental pathway involving high expression of neurogenin 3 (Ngn3) plays a role in the up-regulation of β-cell mass during pregnancy. Using a mouse β-cell lineage-tracing model, which labels insulin-producing β-cells with red fluorescent protein (RFP), we found that the percentage of labeled β-cells dropped from 97% prior to pregnancy to 87% at mid-pregnancy. This suggests contribution of a non-β-cell source to the increase in total β-cell numbers during pregnancy. In addition, we observed a population of hormone-negative, Ngn3-positive cells in islets of both non-pregnant and pregnant mice, and this population dropped from 12% of all islets cells in the non-pregnant mice to 5% by day 8 of pregnancy. Concomitantly, a decrease in expression of Ngn3 and changes in its upstream regulatory network (Sox9 and Hes-1) as well as downstream targets (NeuroD, Nkx2.2, Rfx6 and IA1) were also observed during pregnancy. Our results show that duplication of pre-existing β-cells is not the sole source of new β-cells during pregnancy and that Ngn3 may be involved in this process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".